from datetime import datetime
import pandas as pd
from pathlib import Path
import plotly
import plotly.express as px
import numpy as np
from statsmodels.tsa.api import VAR
import urllib.request
plotly.offline.init_notebook_mode()
NOW = datetime.now()
TODAY = NOW.date()
print('Aktualisiert:', NOW)
Aktualisiert: 2020-12-08 14:09:23.479767
STATE_NAMES = ['Burgenland', 'Kärnten', 'Niederösterreich',
'Oberösterreich', 'Salzburg', 'Steiermark',
'Tirol', 'Vorarlberg', 'Wien']
# TODO: Genauer recherchieren!
EVENTS = {'1. Lockdown': (np.datetime64('2020-03-20'), np.datetime64('2020-04-14'),
'red', 'inside top left'),
'1. Maskenpflicht': (np.datetime64('2020-03-30'), np.datetime64('2020-06-15'),
'yellow', 'inside bottom left'),
'2. Maskenpflicht': (np.datetime64('2020-07-24'), np.datetime64(TODAY),
'yellow', 'inside bottom left'),
'Soft Lockdown': (np.datetime64('2020-11-03'), np.datetime64('2020-11-17'),
'orange', 'inside top left'),
'2. Lockdown': (np.datetime64('2020-11-17'), np.datetime64(TODAY),
'red', 'inside top left')}
def load_data(URL, date_columns):
data_file = Path(URL).name
try:
# Only download the data if we don't have it, to avoid
# excessive server access during local development
with open(data_file):
print("Using local", data_file)
except FileNotFoundError:
print("Downloading", URL)
urllib.request.urlretrieve(URL, data_file)
return pd.read_csv(data_file, sep=';', parse_dates=date_columns, infer_datetime_format=True, dayfirst=True)
raw_data = load_data("https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv", [0])
additional_data = load_data("https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv", [0, 2])
Downloading https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv Downloading https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv
cases = raw_data.query("Bundesland == 'Österreich'")
cases.insert(0, 'AnzahlFaelle_avg7', cases.AnzahlFaelle7Tage / 7)
time = cases.Time
tests = additional_data.query("Bundesland == 'Alle'")
tests.insert(2, 'TagesTests', np.concatenate([[np.nan], np.diff(tests.TestGesamt)]))
tests.insert(3, 'TagesTests_avg7', np.concatenate([[np.nan] * 7, (tests.TestGesamt.values[7:] - tests.TestGesamt.values[:-7])/7]))
tests.insert(0, 'Time', tests.MeldeDatum)
fig = px.line(cases, x='Time', y=["AnzahlFaelle", "AnzahlFaelle_avg7"], log_y=True, title="Fallzahlen")
fig.add_scatter(x=tests.Time, y=tests.TagesTests, name='Tests')
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
all_data = tests.merge(cases, on='Time', how='outer')
all_data.insert(1, 'PosRate', all_data.AnzahlFaelle / all_data.TagesTests)
all_data.insert(1, 'PosRate_avg7', all_data.AnzahlFaelle_avg7 / all_data.TagesTests_avg7)
fig = px.line(all_data, x='Time', y=['PosRate', 'PosRate_avg7'], log_y=False, title="Anteil Positiver Tests")
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
states = []
rates = []
for state_name, state_data in raw_data.groupby('Bundesland'):
x = np.log2(state_data.AnzahlFaelle7Tage)
rate = 2**np.array(np.diff(x))
rates.append(rate)
states.append(state_name)
growth = pd.DataFrame({n: r for n, r in zip(states, rates)})
fig = px.line(growth, x=time[1:], y=STATE_NAMES, title='Wachstumsrate')
fig.update_layout(yaxis=dict(range=[0.25, 4]))
fig.show()
/usr/share/miniconda/lib/python3.8/site-packages/pandas/core/series.py:726: RuntimeWarning: divide by zero encountered in log2 /usr/share/miniconda/lib/python3.8/site-packages/numpy/lib/function_base.py:1280: RuntimeWarning: invalid value encountered in subtract
model = VAR(growth[150:][STATE_NAMES])
res = model.fit(1)
res.summary()
Summary of Regression Results
==================================
Model: VAR
Method: OLS
Date: Tue, 08, Dec, 2020
Time: 14:09:27
--------------------------------------------------------------------
No. of Equations: 9.00000 BIC: -43.3991
Nobs: 134.000 HQIC: -44.5545
Log likelihood: 1416.91 FPE: 2.03145e-20
AIC: -45.3455 Det(Omega_mle): 1.06289e-20
--------------------------------------------------------------------
Results for equation Burgenland
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.500641 0.178393 2.806 0.005
L1.Burgenland 0.138130 0.086077 1.605 0.109
L1.Kärnten -0.294288 0.072319 -4.069 0.000
L1.Niederösterreich 0.117200 0.205018 0.572 0.568
L1.Oberösterreich 0.291076 0.171573 1.697 0.090
L1.Salzburg 0.159047 0.086984 1.828 0.067
L1.Steiermark 0.088800 0.122759 0.723 0.469
L1.Tirol 0.158317 0.081535 1.942 0.052
L1.Vorarlberg 0.004662 0.078817 0.059 0.953
L1.Wien -0.140976 0.163601 -0.862 0.389
======================================================================================
Results for equation Kärnten
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.540820 0.227138 2.381 0.017
L1.Burgenland -0.002386 0.109597 -0.022 0.983
L1.Kärnten 0.344021 0.092081 3.736 0.000
L1.Niederösterreich 0.130608 0.261039 0.500 0.617
L1.Oberösterreich -0.203328 0.218456 -0.931 0.352
L1.Salzburg 0.196300 0.110753 1.772 0.076
L1.Steiermark 0.229353 0.156302 1.467 0.142
L1.Tirol 0.139668 0.103814 1.345 0.179
L1.Vorarlberg 0.210441 0.100354 2.097 0.036
L1.Wien -0.566061 0.208305 -2.717 0.007
======================================================================================
Results for equation Niederösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.305503 0.078034 3.915 0.000
L1.Burgenland 0.106076 0.037652 2.817 0.005
L1.Kärnten -0.017442 0.031634 -0.551 0.581
L1.Niederösterreich 0.128510 0.089680 1.433 0.152
L1.Oberösterreich 0.278236 0.075051 3.707 0.000
L1.Salzburg -0.009601 0.038049 -0.252 0.801
L1.Steiermark -0.042874 0.053698 -0.798 0.425
L1.Tirol 0.089285 0.035665 2.503 0.012
L1.Vorarlberg 0.132115 0.034477 3.832 0.000
L1.Wien 0.037499 0.071563 0.524 0.600
======================================================================================
Results for equation Oberösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.178846 0.090488 1.976 0.048
L1.Burgenland 0.002305 0.043662 0.053 0.958
L1.Kärnten 0.033880 0.036683 0.924 0.356
L1.Niederösterreich 0.053256 0.103993 0.512 0.609
L1.Oberösterreich 0.372247 0.087029 4.277 0.000
L1.Salzburg 0.089623 0.044122 2.031 0.042
L1.Steiermark 0.208303 0.062268 3.345 0.001
L1.Tirol 0.031828 0.041358 0.770 0.442
L1.Vorarlberg 0.109629 0.039979 2.742 0.006
L1.Wien -0.082479 0.082985 -0.994 0.320
======================================================================================
Results for equation Salzburg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.657430 0.193544 3.397 0.001
L1.Burgenland 0.064560 0.093388 0.691 0.489
L1.Kärnten -0.003994 0.078462 -0.051 0.959
L1.Niederösterreich -0.074450 0.222431 -0.335 0.738
L1.Oberösterreich 0.103081 0.186145 0.554 0.580
L1.Salzburg 0.042660 0.094372 0.452 0.651
L1.Steiermark 0.123133 0.133185 0.925 0.355
L1.Tirol 0.226375 0.088459 2.559 0.010
L1.Vorarlberg 0.035650 0.085511 0.417 0.677
L1.Wien -0.154247 0.177496 -0.869 0.385
======================================================================================
Results for equation Steiermark
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.226714 0.133056 1.704 0.088
L1.Burgenland -0.051682 0.064201 -0.805 0.421
L1.Kärnten -0.013887 0.053940 -0.257 0.797
L1.Niederösterreich 0.177348 0.152915 1.160 0.246
L1.Oberösterreich 0.393613 0.127970 3.076 0.002
L1.Salzburg -0.037079 0.064878 -0.572 0.568
L1.Steiermark -0.049746 0.091561 -0.543 0.587
L1.Tirol 0.196739 0.060813 3.235 0.001
L1.Vorarlberg 0.038886 0.058787 0.661 0.508
L1.Wien 0.131716 0.122024 1.079 0.280
======================================================================================
Results for equation Tirol
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.227016 0.170754 1.329 0.184
L1.Burgenland 0.068975 0.082391 0.837 0.403
L1.Kärnten -0.076491 0.069223 -1.105 0.269
L1.Niederösterreich -0.093484 0.196240 -0.476 0.634
L1.Oberösterreich -0.087746 0.164227 -0.534 0.593
L1.Salzburg 0.011179 0.083260 0.134 0.893
L1.Steiermark 0.386052 0.117502 3.285 0.001
L1.Tirol 0.528745 0.078043 6.775 0.000
L1.Vorarlberg 0.225184 0.075442 2.985 0.003
L1.Wien -0.184716 0.156596 -1.180 0.238
======================================================================================
Results for equation Vorarlberg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.120761 0.197316 0.612 0.541
L1.Burgenland 0.030970 0.095208 0.325 0.745
L1.Kärnten -0.086682 0.079991 -1.084 0.279
L1.Niederösterreich 0.145238 0.226766 0.640 0.522
L1.Oberösterreich 0.038973 0.189774 0.205 0.837
L1.Salzburg 0.219159 0.096211 2.278 0.023
L1.Steiermark 0.176849 0.135780 1.302 0.193
L1.Tirol 0.065847 0.090184 0.730 0.465
L1.Vorarlberg 0.032108 0.087178 0.368 0.713
L1.Wien 0.269420 0.180956 1.489 0.137
======================================================================================
Results for equation Wien
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.587766 0.109945 5.346 0.000
L1.Burgenland -0.013081 0.053050 -0.247 0.805
L1.Kärnten 0.004929 0.044571 0.111 0.912
L1.Niederösterreich -0.034249 0.126355 -0.271 0.786
L1.Oberösterreich 0.286610 0.105742 2.710 0.007
L1.Salzburg 0.009431 0.053609 0.176 0.860
L1.Steiermark 0.021488 0.075657 0.284 0.776
L1.Tirol 0.065956 0.050250 1.313 0.189
L1.Vorarlberg 0.176332 0.048576 3.630 0.000
L1.Wien -0.098495 0.100829 -0.977 0.329
======================================================================================
Correlation matrix of residuals
Burgenland Kärnten Niederösterreich Oberösterreich Salzburg Steiermark Tirol Vorarlberg Wien
Burgenland 1.000000 0.101522 -0.028349 0.187722 0.242465 0.017391 0.079904 -0.138637 0.141421
Kärnten 0.101522 1.000000 -0.051799 0.180754 0.099533 -0.166259 0.188262 0.010591 0.264966
Niederösterreich -0.028349 -0.051799 1.000000 0.249839 0.063258 0.171797 0.097918 0.031468 0.373795
Oberösterreich 0.187722 0.180754 0.249839 1.000000 0.254335 0.269477 0.082046 0.060515 0.060649
Salzburg 0.242465 0.099533 0.063258 0.254335 1.000000 0.130711 0.046159 0.079112 -0.049435
Steiermark 0.017391 -0.166259 0.171797 0.269477 0.130711 1.000000 0.083790 0.070895 -0.174421
Tirol 0.079904 0.188262 0.097918 0.082046 0.046159 0.083790 1.000000 0.130826 0.118488
Vorarlberg -0.138637 0.010591 0.031468 0.060515 0.079112 0.070895 0.130826 1.000000 0.054183
Wien 0.141421 0.264966 0.373795 0.060649 -0.049435 -0.174421 0.118488 0.054183 1.000000